Latent classes of students' ICT engagement behaviors: Predictive role of psychological factors based on PISA 2022 data

Objectives This study aims to investigate the latent structure of ICT engagement behaviors among secondary school students in Hong Kong (China), Japan, Korea, and Singapore, and to examine the predictive roles of demographic and psychological factors in determining engagement types. Methods Data from 21,004 students were analyzed using Latent Class Analysis (LCA) to identify underlying ICT engagement profiles. Multinomial logistic regression was employed to evaluate the influence of demographic variables and ICT-related psychological factors on class membership. Results Four distinct ICT engagement profiles emerged: Comprehensive, Routine, Selective, and Limited. Hong Kong (China) students were predominantly Selective or Limited, Japanese students were evenly distributed, Korean students leaned toward Routine and Limited types, and Singaporean students were mainly Comprehensive or Routine participants. Differences were most pronounced in higher-order ICT engagement, while basic ICT engagement was similar across countries/regions. For applied ICT engagement, cognitive and collaborative tasks were more prominent than operational tasks, with Japanese and Korean students favoring higher cognitive and collaborative engagement, and Hong Kong (China) and Singaporean students exhibiting a more balanced profile. High-order ICT tasks highlighted Japanese students' strengths in digital simulation and programming. Regression analyses indicated that ICT-related psychological factors were the strongest predictors, whereas gender and grade exerted partial effects, and ESCS had limited influence. Conclusions Students across the four countries/regions exhibited convergence in basic ICT engagement but divergence in applied and higher-order ICT engagement. These findings provide empirical guidance for designing targeted educational interventions.